class_docstr: Loss for single shot multi-box object detection
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Loss","for","single","shot","multi-box","object","detection","Args",":","opts",":","command-line","arguments"]
code: def extra_repr(self) -> str:
      return (
          f"\n\t neg_pos_ratio={self.neg_pos_ratio}"
          f"\n\t box_loss=SmoothL1"
          f"\n\t class_loss=CrossEntropy"
          f"\n\t self_weighting={True if self.max_iter > 0 else False}"
      )
  
file_path: ml-cvnets/loss_fn/detection/ssd_multibox_loss.py
imports: import argparse
  from typing import Any, Dict
  import torch
  from torch import Tensor
  from torch.nn import functional
  from cvnets.misc.third_party.ssd_utils import hard_negative_mining
  from loss_fn import LOSS_REGISTRY
  from loss_fn.detection.base_detection_criteria import BaseDetectionCriteria
  from utils import logger
  from utils.ddp_utils import is_master
  from utils.tensor_utils import tensor_to_python_float
label_desc: Use image node_id 5 for calling the SSDLoss obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self, str, unknown